Pancreatic cancer is one of the most deadly forms of human malignancy for which early detection would greatly facilitate increased patient survival. A glyco-microarray approach will be used to search for early detection markers of pancreatic cancer in human plasma. We will use multi-dimensional liquid-phase fractionation of intact N-linked plasma glycoproteins isolated by lectin affinity columns. The liquid-based fractionation of intact glycoproteins will initially involve nonporous chromatography, followed by liquid capillary isoelectric focusing to further separate protein glycoforms. Each liquid fraction will be spotted on nitrocellulose- coated microscope slides to produce a natural glycoprotein microarray which will then be interrogated by various fluorescently-labeled lectins to probe each microarray spot for the presence of specific glycan moieties. Plasma samples from cancer and normal patients and patients with inflammatory lesions will be analyzed to search for patterns that reveal specific glycan structural changes that occur during cancer progression. Initial analysis will be performed on an analytical test set of 30 samples each from cancer and normal patients and from patients with related inflammatory lesions (chronic pancreatitis) to identify such potential markers. Novel software will be used to analyze these arrays and the resulting test set. Glycoproteins that are associated with cancer versus normal or inflammation will be identified and the structural moieties identified using lectin extraction/LC ESI/MS/MS and QIT-TOF (MALDI MSn) mass spectrometry to examine the detailed changes in structure that may serve as markers of cancer. While many of the marker glycoproteins themselves are of relatively high abundance in plasma, they may be cancer-specific based upon changes in glycan structure. This research has important implications in public health in that it may provide a methodology for searching for markers of disease in plasma. These markers may be related to the stage of the disease and, thus, may become important for personalized treatment, an important issue in clinical medicine. [unreadable] [unreadable] [unreadable]